Discovering the causes for model-observation discrepancy in tropical tropospheric warming trends

Published in Earth & Environment and Mathematics
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A marked discrepancy in tropical tropospheric warming trends between satellite observations and climate model simulations (Fig. 1) has been a mystery. This indicates model bias and deficiency, undermining the reliability of future climate change projected from generations of climate models. In a new study, published in the journal Communications Earth & Environmentan international team of scientists from South Korea and Germany shows that previous assessment of the model-observation discrepancy is overestimated due to possible satellite biases as well as multi-decadal climate variability over the tropical Pacific.
Figure 1: (a) Spatial pattern of satellite-observed annual-mean tropospheric temperature trends over the period 1979–2014. (b) Same as in (a), but for the ensemble-mean trends from coupled model simulations. (c) Same as in (b), but for atmosphere-only model simulations integrated with observed time-varying sea surface temperatures. (d) Same as in (c), but for a single ensemble member integrated with time-invariant pre-industrial external forcing.

Along with Arctic amplification of near-surface warming resulting from longwave feedback processes, sea ice-albedo feedback and poleward energy transport, enhanced warming in the tropical mid-to-upper troposphere due to moist thermodynamic processes associated with deep convection is one of the most prominent features under increasing concentrations of greenhouse gases. A tug-of-war between near-surface Arctic warming and upper-level tropical warming is known to determine the intensity and location of extratropical jet stream circulating the Northern Hemisphere and thus have far-reaching weather and climate implications. The amplification of greenhouse gas-induced warming in the tropical mid-to-upper troposphere relative to the surface is also a key factor determining the strength of radiative feedback processes, especially lapse-rate and water vapor feedbacks, that act to further amplify or dampen the impact of external forcing. Therefore, it is crucial to ensure that climate models reasonably reproduce observed tropospheric temperature change and variability.

Due to uncertainties in the historical radiosonde temperature records as well as sparse radiosonde observations over the tropical ocean, numerous previous studies assessed the veracity of model-simulated tropospheric temperature change and variability using satellite observed brightness temperatures at microwave frequencies located in the 60-GHz oxygen absorption band (i.e., measurements from the Microwave Sounding Unit (MSU) and Advanced MSU (AMSU) sensors onboard a series of the National Oceanic and Atmospheric Administration (NOAA) operational polar-orbiting satellites) as a benchmark. The magnitude of model-observation discrepancy may vary considerably depending on how to create a homogenized long-term record by merging a series of individual satellite observations. Yet, previous studies unambiguously concluded that most of coupled model simulations markedly overestimate tropical tropospheric warming trends over the satellite era due to model biases and deficiencies. It was found, however, that the interannual relationship between tropical sea surface temperature (SST) in convectively active regions and tropical tropospheric temperature is largely consistent between observations and model simulations. Moreover, prior work indicated that climate models reasonably reproduced satellite-observed trends up to the late 20th century.

Given that long-term climate monitoring was not the main purpose of satellite mission at the outset, part of observational errors might have still remained in spite of various bias-correction and intercalibration processes. In addition, the time span of continuous microwave satellite observations, which started at the end of the 1970s, might not be long enough to unambiguously examine multi-decadal internal climate variability. Based on these aspects, the research team led by Dr. Seong-Joong Kim, Vice President of Korea Polar Research Institute, has explored the possibility that the model-observation discrepancy reported in the literature might be overestimated. By conducting comprehensive analyses over an extended period including the pre-satellite era, they have found that La Niña-like tropical SST trends, which are closely related to internal climate variability (more specifically, the Inter-decadal Pacific Oscillation or Pacific Decadal Oscillation), are responsible for part of the model-observation discrepancy, in agreement with previous studies. However, the overestimation was found to remain over the satellite era in model simulations forced by observed time-varying sea surface temperatures with a La Niña-like pattern (Fig. 1), suggesting the possibility that both model and observational deficiencies contribute to the difference. Indeed, the research team has found a spurious discontinuity in microwave satellite record between the pre-2000 period and the post-2000 period (Fig. 2), which implies that residual satellite biases also contributed to part of the model-observation discrepancy. This study thus suggests that enhancing the reliability of model-projected future climate change is contingent not only on improving the model representation of key physical processes, but also on sustaining an accurate long-term observing system.

Figure 2: Differences between the Remote Sensing Systems (RSS, version 4.0) and other satellite datasets (i.e., the Center for Satellite Applications and Research (STAR, version 5.0), the University of Alabama at Huntsville (UAH, version 6.0), and the University of Washington (UW, version 1.0)) in annual-mean, tropical-mean tropospheric temperature anomaly with respect to the 1979–1988 climatology.

This study attributes the model-observation discrepancies in tropical tropospheric warming trends over the satellite era mainly to internal climate variability and residual biases in the satellite record. However, the research team acknowledges that apart from potential model biases and deficiencies, other factors such as anthropogenic aerosols and land-use changes might have also contributed to the discrepancies. Hence, more in depth analysis is needed to further reduce the discrepancy between satellite observations and climate model simulations.

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Climate Change
Physical Sciences > Earth and Environmental Sciences > Earth Sciences > Climate Sciences > Climate Change
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Mathematics and Computing > Mathematics > Applications of Mathematics > Mathematics of Planet Earth > Climate and Earth System Modelling

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